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Effective Integration of Informatics Tools to Enhance the Drug Discovery Process Hari K. Machina,*,† David J. Wild,*,† Patric Dey,‡ and Mahesh Merchant§ †
Indiana University, School of Informatics: Informatics and Computing Department, 1900 E 10th St, Bloomington, Indiana 47401, United States ‡ Amgen Inc, Corporate Information Systems, One Technology Center Drive, Thousand Oaks, California 91320, United States § Indiana University Perdue University, IU School of Informatics, Indianapolis, Indiana 46202, United States ABSTRACT: Effective integration of data and laboratory informatics tools promises the ability for organizations to make better informed decisions about resource allocation during the drug discovery and development process, and for more informed decisions to be made with respect to the market opportunity for compounds. There are technologies on the horizon that could dramatically change how informatics organizations design, develop, deliver, and support applications and data infrastructures to deliver maximum value to drug discovery organizations. There are numerous benefits of integrating laboratory informatics tools like Laboratory Information Management System (LIMS), Chromatographic Data Systems (CDS), Electronic Laboratory Notebooks (ELN), and Analytical Instrumentation and Scientific Data Management Systems (SDMS) for drug discovery processes, but there is no well-established path for effective integration of these tools. We propose in this article four new integration models for effective integration of laboratory informatics tools.
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BACKGROUND The evolution of the use of informatics technologies in laboratories and drug discovery environments has resulted in the emergence of various kinds of systems, which will be described below. These systems are highly overlapping but have evolved independently; for example, informatics systems from laboratory equipment vendors developed differently than that from the database community.1 An electronic laboratory notebook (ELN) is a system to create, store, retrieve, and share fully electronic records in ways that meet all legal, regulatory, technical, and scientific requirements. The evolution of ELNs changed industries’ traditional way of documenting the analysis and experiments in the physical notebooks. Industry practitioners saw the benefits of ELNs and have implemented them widely since 2002, replacing paper notebooks.1 An ELN for routine analyses takes over at the bench level, right at the point of analysis, providing real-time control and automation of testing procedures. An ELN ensures procedural execution, automates manual processes, collects instrument data, perform calculations, performs limit checking, calibration checking and inventory checking and updating. ELNs also provide electronic documentation and access to test results. These controls ensure compliance with SOPs during the analysis and protect the integrity of collected data.2 The ELN complies with the FDA’s 21 CFR Part 113 and 21 CFR Part 820 guidelines with respect to digital signatures, electronic-records management, and software quality systems management. In using standard PDF format for data storage, export, and printing, the ELN also complies with the FDA’s requirement that electronic copies be readily available for inspection. A Laboratory Information Management System (LIMS) is a software-based system that offers features that support a © 2013 American Chemical Society
modern laboratory’s operations, including workflow and data tracking support, flexible architecture, and smart data exchange interfaces, which support their use in regulated environments. LIMS has existed in the industrial laboratories for over two decades, and has been customized and configured with the constant changing business requirements/processes. In late 90s, LIMS was deployed globally for different manufacturing sites of the same company to share the data within the organization.1 LIMS is focused on information management, storage, and reportinglogging sample information, test information, test results, instrument calibration, chemical inventory, billing information, and so forth. LIMS gets involved at the beginning of the analytical process when samples are logged in and tests are scheduled. LIMS comes back into play when the tests are completed, collecting, storing and reporting results. Some recently introduced LIMS can be also used as an electronic laboratory notebook, since they can perform recording and sharing information captured from disparate sources including instrumental, graphical, and statistical data, office automation formats (.doc, .ppt, .xls, etc.), voice, pictures, and so on. The new LIMS enable lab personnel to securely capture and store a wide range of knowledge and procedures making all captured information readily available for further references. The new LIMS can also be used as scientific data management system (SDMS) and provide a laboratory with a single, seamless access point to all laboratory data. However, currently, the pharmaceutical and biotechnology industry is not following this path because of legacy systems that are currently in use. The LIMS as such will exist for many years to come, it Received: Revised: Accepted: Published: 16547
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accumulated interpretive knowledge or actionable data are generally retained in silos. The availability of data from varied and heterogeneous sources, coupled with the desire to build knowledge bases and collaborative networks, has driven the need for improved integration techniques at the fundamental data level.7 Simple data points are easier to put into databases that have higher level knowledge and insight. There has been a lot of activity in the public sphere for the storage of screening data in databases like PubChem, which aims to offer comprehensive information on the biological activities of small molecules, including the results of high-throughput screening to assess the effects of compounds on target proteins. Some efforts have also been made to integrate the data sets: the majority of NCBI databases are linked through its Entrez search engine, which provides integrated access to literature, sequence, mapping, taxonomy, and structural data. Public databases like PubChem facilitate the process of research and discovery by linking records and terms to related information across NCBI databases. However, higher levels of knowledge and interpretationfor example, analysis of a screen or scientific insightsis generally not captured. This knowledge is still in silos with scientists or buried in their notebooks or in ELNs. All downstream activities, such as information exploitation and knowledge management, are crucially dependent on the effective integration of data and tools. Many of the applications for storing and retrieving chemical data have grown out of the rapid developments in chemical structure coding and searching. The advances in structure-based applications have led to integrated chemical information systems, more and more of which have web interfaces, and to specialized applications such as LIMS. The ability to search large secondary databases and to move seamlessly back and forth between the original primary journal literature and the abstracting and indexing databases is one of the truly great achievements of modern cheminformatics research.8 Pharmaceutical companies spend significant and duplicated efforts aligning and integrating internal information with public data sources. This process is largely incompatible with massive computational approaches, and the vast majority of drug discovery sources cannot easily interoperate.9 There are barriers between public data and internal data, and public data is itself stored in silos, although this has been addressed recently with integrative semantic resources such as Chem2Bio2RDF10 Though the semantic approach has been delivered in small-scale and targeted approaches so far, its promise for multiscale data integration has remained largely unfulfilled. Bottlenecks in Drug Discovery. Typical Steps in the drug discovery process are disease selection, target hypothesis, lead compound identification (screening), lead optimization, preclinical trial, and clinical trial and pharmacogenomic optimization. Traditionally, these steps are carried out sequentially, and if one of the steps is slow, it slows down the entire process. The following slow steps are bottlenecks: • A siloed approach to the adoption of technology • Teams work in isolation on tightly defined projects, often operating independently, or even competitively. Manual data entry and stand-alone systems also make it difficult. Resolution of Bottlenecks. Bottlenecks in the drug discovery process can be eliminated by • Effective integration of informatics tools for the drug discovery process. • The adoption of integrated software infrastructure for the storage and sharing of information enhances communi-
might replace the ELN and the SDMS by adding more functionality to the application as add on modules like STARLIMS.4 Chromatography Data Systems (CDS) collect, manage, and report chromatography test results with a software. A chromatography data software package is used for advanced data acquisition, distributed processing, reporting, and management of samples, as well as complete compliance with 21 CFR Part 11 regulated environments.5 Scientific Data Management System (SDMS) provides scientific content management for all types of scientific data and documentation. SDMS easily integrates with existing informatics systems such as LIMS, ELN, ERP, and instrument data systems.
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INTRODUCTION From initial discovery to a marketable medicine is a long, challenging road. It takes about 12−15 years from discovery to the approved medication and requires an investment of about US $1 billion. On average, from more than a million screened molecules, only one is investigated in late stage clinical trials and is finally made available for patients. Laboratories generate lot of data from various instruments with the help of different software. The process of drug discovery involves the identification of candidates, synthesis, characterization, screening, and assays for therapeutic efficacy. Once a compound has shown its value in these tests, it will begin the process of drug development prior to clinical trials. Despite advances in technology and understanding of biological systems, drug discovery is still a long process with a low rate of new therapeutic discovery due to lack of smart data integration.1 Pharmaceutical companies pay a big price for their inability to get data where it is really needed, especially during drug discovery and clinical trials. Often, the same data is re-entered during each step because standalone, automated systems are not designed or built to share information. New techniques have been designed and implemented to capitalize on the growing amount of information stored in the databases due to increasing amount of public data.1 Dominic John from Accelrys6 thinks that replacing paperbased processes with electronic workflow and process documentation is also a critical step in improving innovation productivity. Paper processes are inefficient and prone to errors; they are also not searchable, not traceable, and they hinder collaboration and information sharing. In contrast, a modern informatics system built on a common platform with integrated electronic laboratory notebooks supports a Quality By Design (QbD) strategy that lowers compliance costs and improves product quality―catalyzing the delivery of better therapeutic products faster and at lower cost to patients.6 There are different integration models, that is, Work flow based Architecture, Enterprise Modular Integration, Integration by Service-Oriented Architecture (SOA), Semantic Repositories, and Nano publications. The ideal integration depends on the type of the laboratory (i.e., analytical laboratory, synthesis laboratory, clinical laboratory, discovery laboratory, research laboratory, etc.) and the type of data generated and the tools used that need to be integrated.1 Data warehousing and mining techniques allow better and more effective analysis of millions of data points. But the research conducted in organizations is generally decentralized, with teams working in various geographical locations and therapeutic areas. The data generated by these teams and the 16548
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implements projects and programs. Technologies such as virtualization and SAS change the way we look at products, their implementation, and overall product life cycle. Unless we move from piecemeal implementations to planned integrated systems, both lab- and corporate-wide, the real benefits of these and other developing technologies will elude us. Four Integration Models. In this paper, we are proposing four different integration models 1. ELN-centric laboratory informatics tools integration model 2. LIMS-centric laboratory informatics tools integration model 3. Hybrid integration model: LIMS/ELN 4. Unified integration model The first integration model is ELN-centric laboratory informatics tools integration model; this model was initially proposed by current authors Machina and Wild in JALA.1
cation that exacerbates productivity loss and wasted research funds. • Automation of image analysis can help streamline research. Limitations of the Paper Notebooks. According to Lass,11 the needs created by collaborative work are hampered by the functionality of paper notebooks. Sharing information recorded in separate notebooks requires a scientist to copy the information and send it to the requesting scientist. This takes time and effort and distracts the scientist from his primary work. It also creates issues for the scientist receiving and using the information. It is hard to record and preserve the audit trail for the information contained within the copied pages. Paper notebooks do not lend themselves to easy searching. Although external databases can correlate notebook numbers to users, the content of paper-based experiments is not indexed for searching. Thus, it is not possible to find experiments that used a specific target compound or all experiments using a specific pathway for synthesis.11 Paper notebooks also present issues when recording information in the same experiment or notebook requires physical proximity of the scientists. This limits where scientists can collaborate and who can collaborate. Signing and witnessing can also cause issues if it is not clear who is responsible for specific entries within an experiment. Scientists have been literally cutting and pasting these computer printouts. It has been estimated that depending on the area of research, scientists pasted 25% to 30% of printouts. The notebooks easily swell to triple their original size. It is a nightmare for archivists, not to mention a frustration to the intellectual property staff. Manually copying documentation into a paper notebook is counterproductive and takes up the valuable time of the highly qualified workforce. Worse still, in the growing area of in-silico studies, or in studies where massive amounts of digital raw data are generated, the mere idea of putting everything down in a bound paper notebook seems absurd. In addition, bound lab notebooks, sequential in nature, are ill suited for documenting events occurring automatically and simultaneously, such as parallel synthesis. The result of all this is a steadily decreasing quality of documentation that needs to be addressed immediately. Benefits of Integrating Laboratory and Corporate Systems. Benefits include • A reduction in management overhead in the lab: interruptions from requests for sample status would be reduced, as would requests for administrative information. • Further, accounting would not need to ask lab managers to create reports. • Having lab-generated information available in electronic format would make it easier and faster (and less expensive) to create shipping certificates of analysis and provide production with the data they require for process monitoring and control. • Well-planned and integrated automation systems would streamline the overall production process. In this age of lab informatics and automation, we need to adopt a new model for how laboratories operate and cooperate with other parts of an organization. Computing, informatics, and automation are more than incremental improvements in how laboratory work gets done. They are the basis for a complete change in how lab and corporate management plans and
1. ELN-CENTRIC LABORATORY INFORMATICS TOOLS INTEGRATION MODEL To expedite the drug discovery process, the proposed ELNcentric integration will help the chemists to eliminate the redundant and manual processes by automating the characterization and assays for the compounds. This proposed model is ELN-centric, where the laboratory instruments can be registered within the instrument module of the ELN, rather than typical integration through LIMS. The integration of the CDS (bidirectionally), ELN (bidirectionally), LIMS (bidirectionally), SDMS (unidirectional), electronic batch record (EBR) (bidirectionally), Cheminformatic tools (bidirectionally), and PubMed (unidirectional) are shown diagrammatically (Figure 1). The customized adopters are used in case of interfacing the ELN with CDS; the Empower CDS is integrated with ELN with the help of Empower adapter and also instruments are interfaced with ELN with instrument adapters (i.e., network interface or RS232). By using the web services, the public databases such as PubChem and SciFinder
Figure 1. Electronic Laboratory Notebook−centric laboratory informatics tools integrations. 16549
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can be accessed. The final laboratory data will be stored in SDMS and the data can be archived from the SDMS in various ways to evaluate the data by using visualization tools like Spot Fire, trending data, data mining, or virtual screening can be performed. These laboratory informatics tools can also be integrated by using LIMS-centric, rather than ELN-centric integration. The advantages of ELN-centric integration is that all the raw data that is generated during the analysis and research, which resides in ELN can be used for dynamic reporting.1 Advantages of ELN. ELNs can be connected to and integrate with other laboratory informatics systems: sample management (LIMS), analytical instrumentation, chromatographic data system (CDS), and scientific data management (SDMS) systems; modeling and simulation programs; chemical inventory systems and scientific databases; and business-level systems such as document management systems, SAP, and Manufacturing Execution systems.1 The electronic lab environment provides a number of advantages for the scientist • Consistent data input. • Reduce documentation time by half. • Reduce time for review and cycle times. • Easy to search data. • Information capturing and reuse, avoiding repeats and reinvention. • Ability to share experiments. • Custom dynamic reporting. • Eliminate illegibility problems created by hard to read handwriting. • Eliminate problems due to lost or damaged paper forms or paper notebooks. • Reduction of paper storage and retrieval costs. ELN Implementation Success Factors. A successful ELN implementation lets scientists quickly create and edit experiments and, more importantly, share and reuse experiments they or other scientists have created.12 Integration of ELN with other applications essential to their work with in laboratory for compound registration, chromatographic data, LIMS, SDMS, and inventory systems will results in less time spent on manual data entry.1 Managers gain visibility into research to track experiments in progress and gauge efficiency and make appropriate decisions. The business case for an ELN, however, can be harder to make. Business sponsors view ELN deployment as an investment intended to improve research and development efforts and typical laboratory process efficiency. Thus, success is measured by return on investment (ROI), the measure of money saved over a specified period of time.12 Expenses in an ELN implementation include not just the cost of purchasing hardware and software, but configuring systems, instrument integration, training users, method coding, and maintaining systems over time (lifecycle management). Savings are easy to identify, but may be harder to quantify. Factors influencing return on investment include12 • Project efficiency: Are we conducting more experiments? • Scientific efficiency: Are scientists spending more time in doing research and less on cutting and pasting data into notebooks? • Cycle time within projects: How quickly are the samples analyzed, reviewed, and approved? • Rework: How often are we repeating or duplicating experiments?
• Information gathering processes: Is it easier to review research or collect information for intellectual property and regulatory filings? • Systems used: Has the implementation enabled us to consolidate or decommission systems? • Time: By using ELN, are we able to perform any of the above activities quickly? The success can be defined by how well an implementation minimizes expenses while maximizing savings, and improving productivity. Organizations can make a business case for an ELN by looking for streamlined configurations, support for critical document and scientific workflows, cloud computing, cost-effective outsourcing options, integration with or elimination of existing systems, and strategies for achieving lower total costs of ownership.3 Advantages of Proposed Model. This integration model allows the scientists to focus on science, rather than on data entry. It also allows rapid integration with instruments by ELN. Finally, it increases the compliance and efficiency while decreasing costs and turnaround times by • Streamlining audit preparation. • Reducing data entry. • Consolidating information systems. • Automating the right tasks. The proposed model can perform • Method execution • Instrument calibration checks • Data acquisition from instruments • Perform calculations and data processing • Easy data review • Automatic data exchange • Data visualization and reporting Use Cases. To support the four proposed integration models, we developed five uses cases to address the following: (i) important for drug discovery and (ii) difficult to answer right now. The use cases will be detailed by reviewing the following: 1. Question/problem statement. 2. How could the question be answered now (or not)? 3. How could the question be answered by each of the models (or not)? 4. What would be the impact if the proposed model can answer the question? Use Case 1. A laboratory scientist is working on a small number of compounds for a particular therapeutic use which are stored in the ELN. It would be highly beneficial for him to know if there are any related articles in chemistry or biology journals that refer to similar compounds being used for the same or related therapeutic areas. How could the question be answered now (or not)? The scientist has to search the literature externally in chemistry and biology journals to see any related information in regards to the compounds that he/she is currently working on and needs to collect the articles and evaluate the useful information manually. How could the question be answered by each of the models (or not)? See Table 1. What would be the impact if the proposed model can answer the question? The proposed model can streamline and automate research activities in medicinal, analytical, and process 16550
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2. LIMS-CENTRIC LABORATORY INFORMATICS TOOLS INTEGRATION MODEL This proposed model is LIMS-centric, where the laboratory instruments can be registered in LIMS. The integration of the CDS (bidirectionally), ELN (bidirectionally), SDMS (unidirectional), electronic batch record (EBR) (bidirectionally), Cheminformatic tools (bidirectionally), and PubMed (unidirectional) are shown diagrammatically (Figure 2). By using the
Table 1. Use Case 1 proposed model
Can the model address the use case question?
ELN-centric integration LIMS-centric integration LIMS/ELN-centric integration LIMS/ELN/SDMS integration
yes no yes yes
chemistry with discipline specific templates. The scientist can plug in search criteria and retrieve the appropriate information almost immediately, often including information that they may have overlooked if they were working solely with Paper Use Case 2. How are the preclinical development studies across globally distributed project teams and external partners performed during drug discovery process? How could the question be answered now (or not)? The preclinical development stage of drug discovery is an iterative process of screening and testing active pharmaceutical ingredients (APIs) to demonstrate that specific API formulations and dosages can be safely put into animals with desired therapeutic effects. Ever increasing complexity in the evaluation of multiple drug candidates using high-throughput in vivo methods has increased the burden of managing the process efficiently.13 The situation is exacerbated by the pharmaceutical industries’ increased reliance on early selectivity. DMPK-ADMET assessments and the growing need to coordinate complex preclinical studies across globally distributed project teams and external partners working in compound screening cascades, DMPKADMET, in vivo, analytics, biologics,and small molecules research.13 How could the question be answered by each of the models (or not)? See Table 2.
Figure 2. LIMS-centric integration model.
web services, the public databases as PubChem and SciFinder also can be accessed via ELN. The final laboratory data will be stored in SDMS, and the data can be archived from the SDMS in various ways to evaluate the data by using visualization tools like Spot Fire, trending data, or data mining. Advantages of Proposed Model. The proposed model has the following advantages: • Routine data analysis and calculations are automated, allowing scientists to review trends and make informed decisions, rather than continuously repeating the same task over and over again. • By eliminating the need for time-consuming manual data entry, scientists can focus on their core competencies, saving valuable and expensive time while mitigating the risk of costly human errors. Stability Testing Process. A stability study measures the shelf life of a given product by testing a series of samples stored in environmental chambers to simulate accelerated testing. Tests are conducted for samples stored under varying conditions and for varying lengths of time because product stored in a warm, bright room might expire sooner than the same product stored in a cool, dark environment. The requirements for stability testing are described in 21CFR211.166.15 Use Case 3. Stability testing management for clinical and commercial products: A laboratory scientist is preparing stability schedule for sample testing, analyzing data, and reporting of data. Managing multiple sample lots and multiple products for schedules, analyzing data, and reporting data manually is a very time-consuming and tedious activity. It will be beneficial to the scientist to query the samples that need to
Table 2. Use Case 2 proposed model
Can the model address the use case question?
ELN-centric integration LIMS-centric integration LIMS/ELN-centric integration LIMS/ELN/SDMS integration
yes no yes yes
What would be the impact if the proposed model can answer the question? The proposed integration models serves as the central hub for all research activities, enabling scientists to easily collaborate, design, and document experiments and provide reports to management for decision making. Scientists can use the ELN to record individual research, collaborate with colleagues on projects, and share critical weekly updates and experimental results with management. Scientists spend 25% less time on data entry, which enables them to focus more energy on research.13 Organizations that effectively automate and manage the drug discovery process, removing latent periods between cycles, can reduce cycle times, accelerate transfer between cycles,and enhance cascade and capacity management, significantly improving efficiency, productivity, and time to market.14 16551
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be tested in a month, such that the scientist can pull the samples appropriately. Additionally, it will help the scientist if he/she can seamlessly transfer the test results without manual entry of data. At the same time, analyzing the data generated for multiple data points requires a statistical tool, and it will also help the scientist to have an automated tool to generate a certificate of analysis report at the end. How could the question be answered now (or not)? Currently the stability management process is mostly performed manually for schedules, testing, trending, and data reporting How could the question be answered by each of the models (or not)? See Table 3. Table 3. Use Case 3 proposed model
Can the model address the use case question?
ELN-centric integration LIMS-centric integration LIMS/ELN-centric integration LIMS/ELN/SDMS integration
no yes yes yes
Figure 3. Hybrid integration model: LIMS/ELN.
• Unified and integrated platform will eliminate the need for multiple applications. • Enhance availability of laboratory data. • Protects data and intellectual property. • Eliminate duplicate searches. • Eliminate the need to develop and manage expensive interfaces. • Streamline information management. Use Case: 4. Environmental monitoring with in pharmaceutical and biotechnology industries requires planning for sampling, assigning work, collecting samples, processing the samples, reviewing the results, and analyzing results.
What would be the impact if the proposed model can answer the question? By using the LIMS-centric integration model, the scientists can provide a powerful way of managing and reporting the outcome of the studies. By using LIMS that is integrated to ELN,CDS, and SDMS, we can automate and control the entire operation of the stability study management process, including15 • Protocol creation. • Study initiation and management. • Inventory management. • Sample login scheduling. • Future workload reporting. • Stability study reporting. The proposed model can help to increase the compliance and efficiency while decreasing costs and turnaround times by15 • Streamlining audit preparation. • Reducing data entry. • Consolidating information systems. • Automating the right tasks.
How could the question be answered now (or not)? This process of planning for sampling, assigning work, collecting samples, processing the samples, reviewing the results, and analyzing results is typically performed manually and is very time-consuming; it will help microbiologists if the process can be automated and the tools used can be integrated. Environmental monitoring describes the processes and activities that need to take place to characterize and monitor the quality of the environment. Environmental monitoring is used in the preparation of environmental impact assessments, as well as in many circumstances in which human activities carry a risk of harmful effects on the natural environment. All monitoring strategies and programs have reasons and justifications which are often designed to establish the current status of an environment or to establish trends in environmental parameters. In all cases, the results of monitoring will be reviewed, analyzed statistically, and published. The design of a monitoring program must therefore have regard to the final use of the data before monitoring starts.16 How could the question be answered by each of the models (or not). See Table 4. What would be the impact if the proposed model can answer the question? The proposed integration model improves the performance of environmental monitoring (EM) operations by eliminating inefficiencies and reducing the error rates inherent in manual, paper-based systems. By
3. HYBRID INTEGRATION MODEL: LIMS/ELN This proposed model is LIMS/ELN-centric, where the laboratory instruments can be registered in LIMS/ELN. The integration of the CDS (bidirectionally), SDMS (unidirectional), electronic batch record (EBR) (Bidirectionally), Cheminformatic tools (bidirectionally), and PubMed (unidirectional) are shown diagrammatically (Figure 3). By using the web services, the public databases as PubChem and SciFinder also can be accessed via LIMS/ELN. The final laboratory data will be stored in SDMS and the data can be archived from the SDMS in various ways to evaluate the data by using visualization tools like Spot Fire, trending data, or data mining. Advantages of Proposed Model. The proposed model has the following advantages: • With streamlined processes and improved automation, this model offers a unified laboratory informatics platform that contains all ELN and LIMS informatics applications in a single system. 16552
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platform that contains all ELN, SDMS, and LIMS informatics applications in a single system. • Hybrid integrated platform will eliminate the need for three separate applications. • Enhance availability of laboratory data. • Eliminate duplicate searches. • Eliminate the need to develop and manage expensive interfaces. • Streamline information management. Use Case 5. The Pharmaceutical and Biotechnology industries are challenged to improve quality, increase production, and gain profits for organizations and also maintain the regulatory requirements while reducing overall operating costs. Due to economic situation, all the corporations are reducing operating budgets. Satisfying the compliance requirements typically consume up to 30%−35% of total operating budgets in the corporations. How could the question be answered now (or not)? In today’s environment, most manufacturing and laboratory operations rely heavily on manual, paper-based processes that are inefficient and error prone. Transcription errors, failure to take appropriate actions, or taking incorrect actions can impact a company’s ability to comply with agency regulations How could the question be answered by each of the models (or not). See Table 5.
Table 4. Use case 4 proposed model
Can the model address the use case question?
ELN-centric integration LIMS-centric integration LIMS/ELN-centric integration LIMS/ELN/SDMS integration
no yes yes no
removing low value and redundant activities from EM processes, the proposed model delivers a tangible return on investment while serving as a regulatory-compliant platform for managing and reporting EM information. The proposed integration model can provide the establishment of single, common repository for all quality assurance and quality control data, including the work in progress. The proposed model provides a means of managing all monitoring data in a single central place. Quality validation, compliance checking, verifying all data has been received, and sending alerts can be automated. Typical interrogation functionality enables comparison of data sets both temporarily and spatially. The proposed model can also generate regulatory and other reporting capabilities.16
4. HYBRID INTEGRATION MODEL WITH UNIFIED LIMS/ELN/SDMS This proposed model is a LIMS/ELN/SDMS hybrid model where the laboratory instruments can be registered in LIMS/ ELN/SDMS. The integration of the CDS (Bidirectionally), electronic batch record (EBR) (bidirectionally), Cheminformatic tools (bidirectionally), and PubMed (Unidirectional) are shown diagrammatically (Figure 4). By using the web services,
Table 5. Use Case 5 proposed model
Can the model address the use case question?
ELN-centric integration LIMS-centric integration LIMS/ELN-centric integration LIMS/ELN/SDMS integration
yes yes yes yes
What would be the impact if the proposed model can answer the question? By reducing the amount of human intervention in the QA/QC process, risk of noncompliance can be significantly reduced. By automating the standard operating procedures (SOPs) that must be followed during the QA/QC process, management and regulatory agencies can be assured that technicians will not deviate from mandated procedures. In the proposed four models, paperless and automated processes will enhance the company’s ability to manage regulatory compliance and will provide significant operational efficiencies that result in tangible cost savings. As laboratories look to streamline the flow of information, having multiple disparate systems with minimal to no connectivity is no longer an option. The shift to a laboratory fully integrated with the enterprise yields a greatly enriched user experience that allows individuals and organizations to not only manage and capture their data more efficiently and securely but also to simplify their daily workflow.17 At a higher level, the proposed integration models enables management to make decisions based on current and pertinent information.
Figure 4. Hybrid integration model with unified LIMS/ELN/SDMS.
the public databases as PubChem and SciFinder also can be accessed via LIMS/ELN. The final laboratory data will be stored within the same LIMS/ELN/SDMS, and the data can be archived from the system in various ways to evaluate the data by using visualization tools like Spot Fire, trending data, or data mining. Advantages of Proposed Model. The proposed model has the following advantages: • With streamlined processes and improved automation, this model offers a unified laboratory informatics
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CONCLUSION There are a number of technologies on the horizon that could potentially dramatically change how informatics organizations design, develop, deliver, and support applications and data infrastructures to deliver maximum value to drug discovery organizations. Effective integration of data and laboratory informatics tools promises the ability for organizations to make 16553
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(6) Domnic, J. Lab to Commercialization Operational Excellence. Accelrys Blog. 2012 (7) Waller, L. C.; Shah, A.; Nolte, M. Strategies to support drug discovery through integration of systems and data. Drug Discovery Today 2007, 12 (15−16), 634−639. (8) Thomas, E. Basic overview of chemoinformatics. J. Chem. Inf. Model. 2006, 46 (6), 2267−2277. (9) Oprea, T. I.; Taboureau, O.; Bologa, G. C. Of possible cheminformatics futures. J. Comput.-Aided Mol. Des. 2012, 26, 107− 112. (10) Chen, B.; Dong, Z.; Jiao, D.; Wang, H.; Zhu, Q.; Ding, Y.; Wild, D. J. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinf. 2010, 11, 255. (11) Bennett, D. L. Implementing Electronic Lab Notebook. Scientific Computing: Rockaway, NJ, 2011. (12) Machina, H. K.; Wild, D. J. Electronic Laboratory Notebooks Progress and Challenges in Implementation. J. Lab. Autom. 2013, 18 (4), 264−8. (13) Molecular connection: Paper free from day one: ELN drives collaborative research at Kalexsyn, case study FALL 2009 pages 1-3 (14) Pharmaceuticalonline, Accelrys, Inc. Case Study. 2013. (15) Boother, J. Application of LIMS to stability Testing “International Pharmaceutical Industry: 2012, 4 (4), 66. (16) Stribling, J. B.; Davie, S. R. Design of an environmental monitoring programme for the Lake Allatoona/Upper Etowah river watershed. Proceedings of the 2005 Georgia Water Resources Conference; April 25−27, 2005; The University of Georgia: Athens, GA, 2005. (17) Thermo Fisher Scientific. Enterprise connectivity for better decision making. Integration manager, brochure0209 (18) Elliot, M. H. Are ELNs really notebooks. Sci. Comput. Instrum. 2004.
better informed decisions about resource allocation during the drug discovery and development process and to make more informed decisions with respect to the market opportunity for compounds .1 The seamless integration of ELN, LIMS, CDS, and SDMS to other systems in the enterprise can eliminate much of the tedious efforts of manual data aggregation and manipulation. The access to data across different sources allows new data relationships to be uncovered. With the notebook oftentimes at the center of a scientist’s workflow, the desire to integrate with all the systems in the laboratory is very effective.1 Careful thought and design, however, needs to be considered to avoid issues of unmanageable integrations .18 Integrating laboratory informatics tools with enterprise resources can have a profound impact on enterprise research.12 Successful integration of the informatics tools greatly multiplies the effectiveness and return on investment of the enterprise deployments. Substantial time reductions can be achieved when creating and reviewing experiments while data integrity is improved. By providing access to a vast array of information and data, the proposed integration models keep scientists apprised of the latest information affecting their research as well as the availability of resources needed to execute experiments or research. In this way, the effective integration of informatics tools brings a new dimension of efficiency to the research effort, enabling scientists to get the answers they need in the least amount of time. The proposed integration models eliminate physical barriers to sharing data and collaborating in the creation and documentation of experimental work.12
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AUTHOR INFORMATION
Corresponding Authors
*Machina. Phone: 317-490-7002. E-mail:
[email protected]. *D. Wild. Phone: 812-856-1848. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Our sincere thanks to the Indiana University Graduate Faculty members (Research committee for H.M.: Dr. Eric Stolterman; Dr. Selma Sabanovic, and Dr. Ying Ding) for their guidance and feedback.
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ABBREVIATIONS LIMS = Laboratory Information Management System CDS = Chromatographic Data Management System ELN = Electronic Laboratory Notebook SDMS = Scientific Data Management System
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REFERENCES
(1) Machina, H. K.; Wild, D. J. Laboratory Informatics Tools Integration Strategies for Drug Discovery, Integration of LIMS, ELN, CDS, and SDMS. J. Lab. Autom. 2012, 18 (2), 126−36. (2) Shon, J.; Ohkawa, H.; Hammer, J. Scientific workflows as productivity tools for drug discovery. Curr. Opin. Drug Discovery Dev. 2008, 11 (3), 381−388. (3) Weinberg, S. A Quick Guide to ELN Regulatory Requirements. Scientific Computing: Rockaway, NJ, 2012. (4) Gall, R. Use of LIMS to Improve trace Evidence Management in the Crime laboratory. Am. Lab. (Southport, CT, U. S.) 2012, 1−2. (5) Noble, D. Chromatography Data Systems. Anal. Chem. (Washington, DC, U. S.) 1995, 69 (19), 617A−620A. 16554
dx.doi.org/10.1021/ie401934a | Ind. Eng. Chem. Res. 2013, 52, 16547−16554
